The present invention relates to the detection of plants and, in particular, to the parameterization of plants for agricultural technology.
In agricultural technology, detecting plants is of significance wherein herein the so-called phenotyping of plants is to be mentioned. For three-dimensional detection of objects, different methods are common, such as strip light methods or light-slit methods. These methods provide a high spatial three-dimensional resolution. However, with regard to illumination, the same depend on defined environmental conditions. In the strip light method, different light patterns have to be subsequently projected onto the object, while in the light-slit method only one contour line is detected at a given time. Thus, for three-dimensional detection, the object has to be scanned.
Parameter extraction and, in particular, model-based parameter extraction are necessitated for phenotyping plants. Phenotyping is the derivation of a structural description from the appearance of a plant. Currently, phenotyping is an object of research in modern agricultural science, since by now the same is an important tool in agricultural fields, such as plant cultivation or plant production.
Two steps are necessitated for phenotyping. The first step is detection or capturing. First, a measurement system quantitatively detects the structural characteristics of a plant. For fast automated recording of the plant structure, imaging methods are suitable, wherein in particular for detecting the surface geometry of a plant mostly active or passive, typically optical 3D detection methods are used. These are, for example, laser light-slit or time-of flight sensor systems or stereoscopic systems by means of optical cameras. The second step is feature extraction. Normally, the measured values do not correspond to illustrative features of the plant structure. Thus, in the second step, transformation of the measurement values to relevant features takes place. Since the detected amount of data is generally quite large, normally, data reduction takes place in this step. For deriving complex leaf parameters from a measured point cloud, model-based feature extraction is suitable due to the flexible adaptation to different purposes of application. These parameters can be used, for example, for describing effects of a change in a genome of the plant on its appearance.
EP2422297B1 describes a concept where the plant is detected three-dimensionally in color and subsequently a leaf model is adapted to the measurement data. The leaf model is described by a number of parameters. The parameters calculated while adapting the model to the measurement data serve to describe the plant. Thereby, for example, the effect of a change in the genome of the plant on the habit of growth of the plant can be described parametrically.
When using the imaging optical measurement methods for detecting the 3D structure, it is problematic that only the optically accessible part of the plant can be detected. Object areas covered by other parts cannot be optically detected. This is undesirable, in particular when detecting plants, since leaves frequently cover one another as it is in particular the case with dense positioning of the leaves or tight tillering of the plant structure.
Missing object areas result in wrong measurement values. The determined leaf area of an only a partly detected plant, for example, does not correspond to the actual leaf area. On the other hand, an only partly detected plant makes the usage of complex feature extraction methods by using a model-based approach impossible when the same are based on preconditions about completely detected leaves. For example, if changes in the genome of the plant only have an effect on those leaves that are optically not accessible, the influence of the change in the genome on the habit of growth cannot be detected with this procedure.
Generally, it can be said that complete detection with optical means is impossible for plants that do not consist of very few leaves, due to unavoidable coverages.